{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 模块导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LERRY\\Anaconda3\\lib\\site-packages\\statsmodels\\tools\\_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "from scipy import stats\n",
    "from scipy.stats import norm, skew\n",
    "from scipy.special import boxcox1p\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.linear_model import ElasticNet, Lasso, BayesianRidge, LassoLarsIC\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone\n",
    "from sklearn.model_selection import KFold, cross_val_score, train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import xgboost as xgb\n",
    "import lightgbm as lgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#不用print即可输出多个变量\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot中文显示问题\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 数据集合并与保存"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 数据导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义加载数据集的方法\n",
    "def load_dataset(path,inputfile):\n",
    "    return pd.read_csv(path+inputfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#数据所在路径\n",
    "path='data/enterprise_profit_forecast/'\n",
    "#导入所有数据集\n",
    "base_train=load_dataset(path,'base-train.csv')\n",
    "base_test=load_dataset(path,'base-test.csv')\n",
    "know_train=load_dataset(path,'knowledge-train.csv')\n",
    "know_test=load_dataset(path,'knowledge-test.csv')\n",
    "money_train=load_dataset(path,'money-train.csv')\n",
    "money_test=load_dataset(path,'money-test.csv')\n",
    "year_train=load_dataset(path,'year-train.csv')\n",
    "year_test=load_dataset(path,'year-test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "base_train: (17319, 8)\n",
      "know_train: (17319, 4)\n",
      "money_train (51954, 10)\n",
      "year_train (51954, 11)\n",
      "----------------------------------------\n",
      "base_test: (4068, 8)\n",
      "know_test: (4068, 4)\n",
      "money_test (8136, 10)\n",
      "year_test (8136, 11)\n"
     ]
    }
   ],
   "source": [
    "#数据集维度\n",
    "print('base_train:',base_train.shape)\n",
    "print('know_train:',know_train.shape)\n",
    "print('money_train',money_train.shape)\n",
    "print('year_train',year_train.shape)\n",
    "print('-'*40)\n",
    "print('base_test:',base_test.shape)\n",
    "print('know_test:',know_test.shape)\n",
    "print('money_test',money_test.shape)\n",
    "print('year_test',year_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 2.2.1 财报money和年报year数据集处理\n",
    "按年份(2015,2016,2017)拆分财报money和年报year数据集，子集的特征作为独立的特征后合并  \n",
    "拆分前查看year特征缺失情况：可看出只有train的年份有缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据集：year_train\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ID           0\n",
       "year       496\n",
       "从业人数       500\n",
       "资产总额       546\n",
       "负债总额       477\n",
       "营业总收入      513\n",
       "主营业务收入     490\n",
       "利润总额       508\n",
       "净利润        548\n",
       "纳税总额       523\n",
       "所有者权益合计    535\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------\n",
      "数据集：money_train\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ID               0\n",
       "year           525\n",
       "债权融资额度         480\n",
       "债权融资成本         521\n",
       "股权融资额度         525\n",
       "股权融资成本         530\n",
       "内部融资和贸易融资额度    481\n",
       "内部融资和贸易融资成本    502\n",
       "项目融资和政策融资额度    539\n",
       "项目融资和政策融资成本    526\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------\n",
      "数据集：year_test\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ID           0\n",
       "year         0\n",
       "从业人数        71\n",
       "资产总额       100\n",
       "负债总额        87\n",
       "营业总收入       90\n",
       "主营业务收入      77\n",
       "利润总额        69\n",
       "净利润         83\n",
       "纳税总额        79\n",
       "所有者权益合计     93\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------\n",
      "数据集：money_test\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ID              0\n",
       "year            0\n",
       "债权融资额度         86\n",
       "债权融资成本         84\n",
       "股权融资额度         88\n",
       "股权融资成本         86\n",
       "内部融资和贸易融资额度    95\n",
       "内部融资和贸易融资成本    73\n",
       "项目融资和政策融资额度    70\n",
       "项目融资和政策融资成本    87\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "print('数据集：year_train')\n",
    "year_train.isna().sum()\n",
    "print('-'*40)\n",
    "print('数据集：money_train')\n",
    "money_train.isna().sum()\n",
    "print('-'*40)\n",
    "print('数据集：year_test')\n",
    "year_test.isna().sum()\n",
    "print('-'*40)\n",
    "print('数据集：money_test')\n",
    "money_test.isna().sum()\n",
    "print('-'*40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 2.1.2 根据情况精确填充缺失年份"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#找到缺失年份的ID\n",
    "def findIdLackOfYear(df,col_id='ID',col_lack='year'):\n",
    "    return df[col_id][df[col_lack].isna()].values\n",
    "#通过ID填补缺失的年份\n",
    "def fillYearById(df,na_year_id,col_id='ID',col_lack='year'):\n",
    "    res=pd.DataFrame(columns=df.columns.values)\n",
    "    for idx in na_year_id:\n",
    "        temp=df[df[col_id].isin([idx])]\n",
    "        #若年份只缺失了一个，则根据另外两个反推\n",
    "        if temp[col_lack].isna().sum()==1:\n",
    "            if not temp[col_lack].isin([2015]).any():\n",
    "                temp[col_lack][temp[col_lack].isna()]=2015            \n",
    "            elif not temp[col_lack].isin([2016]).any():\n",
    "                temp[col_lack][temp[col_lack].isna()]=2016\n",
    "            elif not temp[col_lack].isin([2017]).any():\n",
    "                temp[col_lack][temp[col_lack].isna()]=2017\n",
    "        res=res.append(temp)\n",
    "    na_id=findIdLackOfYear(res)\n",
    "    res=res[~res[col_id].isin(na_id)]\n",
    "    return res\n",
    "#合并到year数据集\n",
    "def getFilledYear(df,res,col_id='ID'):\n",
    "    return df[~df[col_id].isin(res[col_id].values)].append(res)\n",
    "#精确填充ID缺失的年份\n",
    "def fillAndMerge(df,col_id='ID',col_lack='year'):\n",
    "    na_id=findIdLackOfYear(df)\n",
    "    fill=fillYearById(df,na_id)\n",
    "    return getFilledYear(df,fill)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LERRY\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if sys.path[0] == '':\n",
      "C:\\Users\\LERRY\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:8765: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._update_inplace(new_data)\n",
      "C:\\Users\\LERRY\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\LERRY\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  app.launch_new_instance()\n"
     ]
    }
   ],
   "source": [
    "#填充train的年份缺失值\n",
    "year_train212=fillAndMerge(year_train)\n",
    "money_train212=fillAndMerge(money_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "year_train (51954, 11)\n",
      "year_train311 (51954, 11)\n",
      "money_train (51954, 10)\n",
      "money_train311 (51954, 10)\n"
     ]
    }
   ],
   "source": [
    "#填充后维度和原数据集是否一致\n",
    "print('year_train',year_train.shape)\n",
    "print('year_train212',year_train212.shape)\n",
    "print('money_train',money_train.shape)\n",
    "print('money_train212',money_train212.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 2.1.3 填充后删除year特征仍有缺失值的样本\n",
    "可看出year特征扔含有少量缺失值，原因是该部分样本每个都缺失了不止1个year特征，此时无法精确填充，直接删除该部分样本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据集：year_train311 (51954, 11)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ID           0\n",
       "year        12\n",
       "从业人数       500\n",
       "资产总额       546\n",
       "负债总额       477\n",
       "营业总收入      513\n",
       "主营业务收入     490\n",
       "利润总额       508\n",
       "净利润        548\n",
       "纳税总额       523\n",
       "所有者权益合计    535\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------\n",
      "数据集：money_train311 (51954, 10)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ID               0\n",
       "year             8\n",
       "债权融资额度         480\n",
       "债权融资成本         521\n",
       "股权融资额度         525\n",
       "股权融资成本         530\n",
       "内部融资和贸易融资额度    481\n",
       "内部融资和贸易融资成本    502\n",
       "项目融资和政策融资额度    539\n",
       "项目融资和政策融资成本    526\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#填充后的缺失值统计\n",
    "print('数据集：year_train212',year_train212.shape)\n",
    "year_train212.isna().sum()\n",
    "print('-'*40)\n",
    "print('数据集：money_train212',money_train212.shape)\n",
    "money_train212.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#删除yeart特征含有缺失值的样本\n",
    "year_train213=year_train212.dropna(subset=['year'])\n",
    "money_train213=money_train212.dropna(subset=['year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据集：year_train312 (51942, 11)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ID           0\n",
       "year         0\n",
       "从业人数       500\n",
       "资产总额       546\n",
       "负债总额       476\n",
       "营业总收入      513\n",
       "主营业务收入     490\n",
       "利润总额       508\n",
       "净利润        548\n",
       "纳税总额       522\n",
       "所有者权益合计    534\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------\n",
      "数据集：money_train312 (51946, 10)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ID               0\n",
       "year             0\n",
       "债权融资额度         480\n",
       "债权融资成本         521\n",
       "股权融资额度         525\n",
       "股权融资成本         530\n",
       "内部融资和贸易融资额度    481\n",
       "内部融资和贸易融资成本    502\n",
       "项目融资和政策融资额度    539\n",
       "项目融资和政策融资成本    526\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#填充后的缺失值统计\n",
    "print('数据集：year_train213',year_train213.shape)\n",
    "year_train213.isna().sum()\n",
    "print('-'*40)\n",
    "print('数据集：money_train213',money_train213.shape)\n",
    "money_train213.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 2.1.4 拆分数据集\n",
    "按year特征(2015,2016,2017)拆分财报money和年报year数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义拆分数据集的方法\n",
    "def split_dataset_by(df,col='year'):\n",
    "    \"\"\"\n",
    "    df：要处理的数据集的dataframe\n",
    "    col：要按哪一列处理\n",
    "    return:拆分后的数据集dataframe组成的列表\n",
    "    \"\"\"\n",
    "    split_df=[]\n",
    "    col_values=np.sort(df[col].value_counts().index.values)#col这一列的唯一值\n",
    "    columns=df.columns.values.tolist()#df所有列名\n",
    "    columns.remove(col)#删除掉要拆分的这一列的名称col\n",
    "    #循环拆分数据集\n",
    "    for col_value in col_values:\n",
    "        col_value=int(col_value)\n",
    "        locals()[col+str(col_value)]=df[df[col].isin([col_value])].drop([col],axis=1)\n",
    "        new_columns=['ID']\n",
    "        for column in columns:\n",
    "            if column=='ID':\n",
    "                continue\n",
    "            new_columns.append(column+str(col_value))\n",
    "        locals()[col+str(col_value)].columns=new_columns#拆分出来的数据集重新命名它的列名称\n",
    "        split_df.append(locals()[col+str(col_value)])#处理完成的数据集添加到列表中\n",
    "    return split_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#按年份拆分money_train,year_train,money_test,year-test四个数据集\n",
    "split_money_train=split_dataset_by(money_train213)\n",
    "split_year_train=split_dataset_by(year_train213)\n",
    "split_money_test=split_dataset_by(money_test)\n",
    "split_year_test=split_dataset_by(year_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 2.1.5 合并拆分数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义合并数据集的方法\n",
    "def merge_dataset(dfs,col='ID'):\n",
    "    \"\"\"\n",
    "    df：要处理的数据集的dataframe\n",
    "    col：要按哪一列处理\n",
    "    return:合并后的数据集dataframe\n",
    "    \"\"\"\n",
    "    df_res=pd.DataFrame()\n",
    "    for df in dfs:\n",
    "        if len(df_res)==0:\n",
    "            df_res=df\n",
    "        else:\n",
    "            df_res=pd.merge(df_res,df,on=col,how='outer')#按col列合并，outer表示保留两个表的信息\n",
    "    return df_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#分别合并money_train,year_train拆分后的数据集\n",
    "money_train_res=merge_dataset(split_money_train)\n",
    "year_train_res=merge_dataset(split_year_train)\n",
    "money_test_res=merge_dataset(split_money_test)\n",
    "year_test_res=merge_dataset(split_year_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据集：money_train_res (17318, 25)\n",
      "数据集：year_train_res (17318, 28)\n",
      "数据集：money_test_res (4068, 17)\n",
      "数据集：year_test_res (4068, 19)\n"
     ]
    }
   ],
   "source": [
    "print('数据集：money_train_res',money_train_res.shape)\n",
    "print('数据集：year_train_res',year_train_res.shape)\n",
    "print('数据集：money_test_res',money_test_res.shape)\n",
    "print('数据集：year_test_res',year_test_res.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#合并所有数据集为train,test\n",
    "train=merge_dataset([base_train,know_train,money_train_res,year_train_res])\n",
    "test=merge_dataset([base_test,know_test,money_test_res,year_test_res])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#删除train中除了'利润2017'以外其他2017年的特征\n",
    "#train.columns\n",
    "#test.columns\n",
    "cols215=test.columns.values.tolist()\n",
    "cols215.append('净利润2017')\n",
    "train215=train.loc[:,cols215].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 2.1.6 保存总数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17319, 46)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(4068, 45)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>注册时间</th>\n",
       "      <th>注册资本</th>\n",
       "      <th>行业</th>\n",
       "      <th>区域</th>\n",
       "      <th>企业类型</th>\n",
       "      <th>控制人类型</th>\n",
       "      <th>控制人持股比例</th>\n",
       "      <th>专利</th>\n",
       "      <th>商标</th>\n",
       "      <th>...</th>\n",
       "      <th>从业人数2016</th>\n",
       "      <th>资产总额2016</th>\n",
       "      <th>负债总额2016</th>\n",
       "      <th>营业总收入2016</th>\n",
       "      <th>主营业务收入2016</th>\n",
       "      <th>利润总额2016</th>\n",
       "      <th>净利润2016</th>\n",
       "      <th>纳税总额2016</th>\n",
       "      <th>所有者权益合计2016</th>\n",
       "      <th>净利润2017</th>\n",
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       "      <td>5981446</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>1190.0</td>\n",
       "      <td>服务业</td>\n",
       "      <td>湖南</td>\n",
       "      <td>农民专业合作社</td>\n",
       "      <td>自然人</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>631.0</td>\n",
       "      <td>47600.0</td>\n",
       "      <td>69615.0</td>\n",
       "      <td>52360.0</td>\n",
       "      <td>20944.0</td>\n",
       "      <td>15708.0</td>\n",
       "      <td>-5236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-22015.0</td>\n",
       "      <td>8353.8</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5993858</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>6670.0</td>\n",
       "      <td>零售业</td>\n",
       "      <td>湖南</td>\n",
       "      <td>集体所有制企业</td>\n",
       "      <td>企业法人</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>353.0</td>\n",
       "      <td>106720.0</td>\n",
       "      <td>150075.0</td>\n",
       "      <td>74704.0</td>\n",
       "      <td>37352.0</td>\n",
       "      <td>29881.6</td>\n",
       "      <td>-7470.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-43355.0</td>\n",
       "      <td>-31215.6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5990025</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>9100.0</td>\n",
       "      <td>社区服务</td>\n",
       "      <td>湖北</td>\n",
       "      <td>农民专业合作社</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.51</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>84.0</td>\n",
       "      <td>291200.0</td>\n",
       "      <td>282100.0</td>\n",
       "      <td>145600.0</td>\n",
       "      <td>87360.0</td>\n",
       "      <td>29120.0</td>\n",
       "      <td>43680.0</td>\n",
       "      <td>87360.0</td>\n",
       "      <td>9100.0</td>\n",
       "      <td>22932.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5982670</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>5870.0</td>\n",
       "      <td>商业服务业</td>\n",
       "      <td>广东</td>\n",
       "      <td>股份有限公司</td>\n",
       "      <td>企业法人</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>965.0</td>\n",
       "      <td>410900.0</td>\n",
       "      <td>607545.0</td>\n",
       "      <td>1150520.0</td>\n",
       "      <td>920416.0</td>\n",
       "      <td>115052.0</td>\n",
       "      <td>230104.0</td>\n",
       "      <td>230104.0</td>\n",
       "      <td>-196645.0</td>\n",
       "      <td>162012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5990751</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>9850.0</td>\n",
       "      <td>工业</td>\n",
       "      <td>江西</td>\n",
       "      <td>集体所有制企业</td>\n",
       "      <td>企业法人</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>213.0</td>\n",
       "      <td>325050.0</td>\n",
       "      <td>472800.0</td>\n",
       "      <td>1137675.0</td>\n",
       "      <td>682605.0</td>\n",
       "      <td>341302.5</td>\n",
       "      <td>341302.5</td>\n",
       "      <td>455070.0</td>\n",
       "      <td>-147750.0</td>\n",
       "      <td>302592.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
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      "text/plain": [
       "        ID    注册时间    注册资本     行业  区域     企业类型 控制人类型  控制人持股比例   专利   商标  ...  \\\n",
       "0  5981446  2008.0  1190.0    服务业  湖南  农民专业合作社   自然人     0.91  1.0  NaN  ...   \n",
       "1  5993858  2008.0  6670.0    零售业  湖南  集体所有制企业  企业法人     0.91  0.0  0.0  ...   \n",
       "2  5990025  2008.0  9100.0   社区服务  湖北  农民专业合作社   NaN     0.51  1.0  0.0  ...   \n",
       "3  5982670  2002.0  5870.0  商业服务业  广东   股份有限公司  企业法人     0.63  1.0  0.0  ...   \n",
       "4  5990751  2005.0  9850.0     工业  江西  集体所有制企业  企业法人     0.58  0.0  0.0  ...   \n",
       "\n",
       "   从业人数2016  资产总额2016  负债总额2016  营业总收入2016  主营业务收入2016  利润总额2016   净利润2016  \\\n",
       "0     631.0   47600.0   69615.0    52360.0     20944.0   15708.0   -5236.0   \n",
       "1     353.0  106720.0  150075.0    74704.0     37352.0   29881.6   -7470.4   \n",
       "2      84.0  291200.0  282100.0   145600.0     87360.0   29120.0   43680.0   \n",
       "3     965.0  410900.0  607545.0  1150520.0    920416.0  115052.0  230104.0   \n",
       "4     213.0  325050.0  472800.0  1137675.0    682605.0  341302.5  341302.5   \n",
       "\n",
       "   纳税总额2016  所有者权益合计2016   净利润2017  \n",
       "0       0.0     -22015.0    8353.8  \n",
       "1       0.0     -43355.0  -31215.6  \n",
       "2   87360.0       9100.0   22932.0  \n",
       "3  230104.0    -196645.0  162012.0  \n",
       "4  455070.0    -147750.0  302592.0  \n",
       "\n",
       "[5 rows x 46 columns]"
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     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
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       "      <th>0</th>\n",
       "      <td>5992473</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>8670.0</td>\n",
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       "      <td>广东</td>\n",
       "      <td>有限责任公司</td>\n",
       "      <td>企业法人</td>\n",
       "      <td>0.54</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-8670.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>52020.0</td>\n",
       "      <td>86700.0</td>\n",
       "      <td>20808.0</td>\n",
       "      <td>12484.8</td>\n",
       "      <td>6242.4</td>\n",
       "      <td>2080.8</td>\n",
       "      <td>4161.6</td>\n",
       "      <td>-34680.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5999375</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>服务业</td>\n",
       "      <td>广东</td>\n",
       "      <td>集体所有制企业</td>\n",
       "      <td>企业法人</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>18700.0</td>\n",
       "      <td>518.0</td>\n",
       "      <td>61200.0</td>\n",
       "      <td>86700.0</td>\n",
       "      <td>48960.0</td>\n",
       "      <td>39168.0</td>\n",
       "      <td>24480.0</td>\n",
       "      <td>-4896.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-25500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5981121</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>3350.0</td>\n",
       "      <td>商业服务业</td>\n",
       "      <td>福建</td>\n",
       "      <td>有限责任公司</td>\n",
       "      <td>企业法人</td>\n",
       "      <td>0.51</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10050.0</td>\n",
       "      <td>931.0</td>\n",
       "      <td>33500.0</td>\n",
       "      <td>60300.0</td>\n",
       "      <td>123950.0</td>\n",
       "      <td>74370.0</td>\n",
       "      <td>24790.0</td>\n",
       "      <td>12395.0</td>\n",
       "      <td>49580.0</td>\n",
       "      <td>-26800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5978928</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>7690.0</td>\n",
       "      <td>工业</td>\n",
       "      <td>广西</td>\n",
       "      <td>股份有限公司</td>\n",
       "      <td>企业法人</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-46140.0</td>\n",
       "      <td>749.0</td>\n",
       "      <td>61520.0</td>\n",
       "      <td>107660.0</td>\n",
       "      <td>307600.0</td>\n",
       "      <td>153800.0</td>\n",
       "      <td>92280.0</td>\n",
       "      <td>-30760.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-46140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5979981</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>4850.0</td>\n",
       "      <td>交通运输业</td>\n",
       "      <td>广西</td>\n",
       "      <td>股份有限公司</td>\n",
       "      <td>企业法人</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4850.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>72750.0</td>\n",
       "      <td>33950.0</td>\n",
       "      <td>189150.0</td>\n",
       "      <td>113490.0</td>\n",
       "      <td>75660.0</td>\n",
       "      <td>75660.0</td>\n",
       "      <td>113490.0</td>\n",
       "      <td>38800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID    注册时间    注册资本     行业  区域     企业类型 控制人类型  控制人持股比例   专利   商标  ...  \\\n",
       "0  5992473  2010.0  8670.0  交通运输业  广东   有限责任公司  企业法人     0.54  1.0  1.0  ...   \n",
       "1  5999375  2010.0  3400.0    服务业  广东  集体所有制企业  企业法人     0.90  0.0  1.0  ...   \n",
       "2  5981121  2014.0  3350.0  商业服务业  福建   有限责任公司  企业法人     0.51  1.0  1.0  ...   \n",
       "3  5978928  2012.0  7690.0     工业  广西   股份有限公司  企业法人     0.81  0.0  0.0  ...   \n",
       "4  5979981  2013.0  4850.0  交通运输业  广西   股份有限公司  企业法人     0.61  1.0  0.0  ...   \n",
       "\n",
       "   所有者权益合计2015  从业人数2016  资产总额2016  负债总额2016  营业总收入2016  主营业务收入2016  利润总额2016  \\\n",
       "0      -8670.0     223.0   52020.0   86700.0    20808.0     12484.8    6242.4   \n",
       "1      18700.0     518.0   61200.0   86700.0    48960.0     39168.0   24480.0   \n",
       "2      10050.0     931.0   33500.0   60300.0   123950.0     74370.0   24790.0   \n",
       "3     -46140.0     749.0   61520.0  107660.0   307600.0    153800.0   92280.0   \n",
       "4       4850.0     135.0   72750.0   33950.0   189150.0    113490.0   75660.0   \n",
       "\n",
       "   净利润2016  纳税总额2016  所有者权益合计2016  \n",
       "0   2080.8    4161.6     -34680.0  \n",
       "1  -4896.0       0.0     -25500.0  \n",
       "2  12395.0   49580.0     -26800.0  \n",
       "3 -30760.0       0.0     -46140.0  \n",
       "4  75660.0  113490.0      38800.0  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看总数据集\n",
    "train216.shape\n",
    "test.shape\n",
    "train216.head()\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "train216=train214\n",
    "test216=test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "path216='data/enterprise_profit_forecast/all/'\n",
    "train216.to_csv(path216+'train216.txt',index=False)\n",
    "test216.to_csv(path216+'test216.txt',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 总数据集导入与预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 数据导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "path31='data/enterprise_profit_forecast/all/'\n",
    "train31=pd.read_csv(path31+'train216.txt')\n",
    "test31=pd.read_csv(path31+'test216.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>注册时间</th>\n",
       "      <th>注册资本</th>\n",
       "      <th>行业</th>\n",
       "      <th>区域</th>\n",
       "      <th>企业类型</th>\n",
       "      <th>控制人类型</th>\n",
       "      <th>控制人持股比例</th>\n",
       "      <th>专利</th>\n",
       "      <th>商标</th>\n",
       "      <th>...</th>\n",
       "      <th>从业人数2016</th>\n",
       "      <th>资产总额2016</th>\n",
       "      <th>负债总额2016</th>\n",
       "      <th>营业总收入2016</th>\n",
       "      <th>主营业务收入2016</th>\n",
       "      <th>利润总额2016</th>\n",
       "      <th>净利润2016</th>\n",
       "      <th>纳税总额2016</th>\n",
       "      <th>所有者权益合计2016</th>\n",
       "      <th>净利润2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5981446</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>1190.0</td>\n",
       "      <td>服务业</td>\n",
       "      <td>湖南</td>\n",
       "      <td>农民专业合作社</td>\n",
       "      <td>自然人</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>631.0</td>\n",
       "      <td>47600.0</td>\n",
       "      <td>69615.0</td>\n",
       "      <td>52360.0</td>\n",
       "      <td>20944.0</td>\n",
       "      <td>15708.0</td>\n",
       "      <td>-5236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-22015.0</td>\n",
       "      <td>8353.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5993858</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>6670.0</td>\n",
       "      <td>零售业</td>\n",
       "      <td>湖南</td>\n",
       "      <td>集体所有制企业</td>\n",
       "      <td>企业法人</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>353.0</td>\n",
       "      <td>106720.0</td>\n",
       "      <td>150075.0</td>\n",
       "      <td>74704.0</td>\n",
       "      <td>37352.0</td>\n",
       "      <td>29881.6</td>\n",
       "      <td>-7470.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-43355.0</td>\n",
       "      <td>-31215.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5990025</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>9100.0</td>\n",
       "      <td>社区服务</td>\n",
       "      <td>湖北</td>\n",
       "      <td>农民专业合作社</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.51</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>84.0</td>\n",
       "      <td>291200.0</td>\n",
       "      <td>282100.0</td>\n",
       "      <td>145600.0</td>\n",
       "      <td>87360.0</td>\n",
       "      <td>29120.0</td>\n",
       "      <td>43680.0</td>\n",
       "      <td>87360.0</td>\n",
       "      <td>9100.0</td>\n",
       "      <td>22932.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5982670</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>5870.0</td>\n",
       "      <td>商业服务业</td>\n",
       "      <td>广东</td>\n",
       "      <td>股份有限公司</td>\n",
       "      <td>企业法人</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>965.0</td>\n",
       "      <td>410900.0</td>\n",
       "      <td>607545.0</td>\n",
       "      <td>1150520.0</td>\n",
       "      <td>920416.0</td>\n",
       "      <td>115052.0</td>\n",
       "      <td>230104.0</td>\n",
       "      <td>230104.0</td>\n",
       "      <td>-196645.0</td>\n",
       "      <td>162012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5990751</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>9850.0</td>\n",
       "      <td>工业</td>\n",
       "      <td>江西</td>\n",
       "      <td>集体所有制企业</td>\n",
       "      <td>企业法人</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>213.0</td>\n",
       "      <td>325050.0</td>\n",
       "      <td>472800.0</td>\n",
       "      <td>1137675.0</td>\n",
       "      <td>682605.0</td>\n",
       "      <td>341302.5</td>\n",
       "      <td>341302.5</td>\n",
       "      <td>455070.0</td>\n",
       "      <td>-147750.0</td>\n",
       "      <td>302592.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID    注册时间    注册资本     行业  区域     企业类型 控制人类型  控制人持股比例   专利   商标  ...  \\\n",
       "0  5981446  2008.0  1190.0    服务业  湖南  农民专业合作社   自然人     0.91  1.0  NaN  ...   \n",
       "1  5993858  2008.0  6670.0    零售业  湖南  集体所有制企业  企业法人     0.91  0.0  0.0  ...   \n",
       "2  5990025  2008.0  9100.0   社区服务  湖北  农民专业合作社   NaN     0.51  1.0  0.0  ...   \n",
       "3  5982670  2002.0  5870.0  商业服务业  广东   股份有限公司  企业法人     0.63  1.0  0.0  ...   \n",
       "4  5990751  2005.0  9850.0     工业  江西  集体所有制企业  企业法人     0.58  0.0  0.0  ...   \n",
       "\n",
       "   从业人数2016  资产总额2016  负债总额2016  营业总收入2016  主营业务收入2016  利润总额2016   净利润2016  \\\n",
       "0     631.0   47600.0   69615.0    52360.0     20944.0   15708.0   -5236.0   \n",
       "1     353.0  106720.0  150075.0    74704.0     37352.0   29881.6   -7470.4   \n",
       "2      84.0  291200.0  282100.0   145600.0     87360.0   29120.0   43680.0   \n",
       "3     965.0  410900.0  607545.0  1150520.0    920416.0  115052.0  230104.0   \n",
       "4     213.0  325050.0  472800.0  1137675.0    682605.0  341302.5  341302.5   \n",
       "\n",
       "   纳税总额2016  所有者权益合计2016   净利润2017  \n",
       "0       0.0     -22015.0    8353.8  \n",
       "1       0.0     -43355.0  -31215.6  \n",
       "2   87360.0       9100.0   22932.0  \n",
       "3  230104.0    -196645.0  162012.0  \n",
       "4  455070.0    -147750.0  302592.0  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train31.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17319, 46)\n",
      "(4068, 45)\n"
     ]
    }
   ],
   "source": [
    "#ID特征处理\n",
    "#处理前形状\n",
    "print(train31.shape)\n",
    "print(test31.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_Id = train31['ID']\n",
    "test_Id = test31['ID']\n",
    "train31.drop('ID', axis=1, inplace=True)\n",
    "test31.drop('ID', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17319, 45)\n",
      "(4068, 44)\n"
     ]
    }
   ],
   "source": [
    "#处理后形状\n",
    "print(train31.shape)\n",
    "print(test31.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 特征工程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.2.1 数据集连接"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all_data size is (21387, 44)\n"
     ]
    }
   ],
   "source": [
    "ntrain = train31.shape[0]\n",
    "ntest = test31.shape[0]\n",
    "y_train = train31['净利润2017'].values\n",
    "all_data = pd.concat((train31, test31)).reset_index(drop=True)\n",
    "all_data.drop(['净利润2017'], axis=1, inplace=True)\n",
    "print('all_data size is {}'.format(all_data.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.2.2 缺失值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "注册时间               228\n",
       "注册资本               215\n",
       "行业                 194\n",
       "区域                 204\n",
       "企业类型               216\n",
       "控制人类型              237\n",
       "控制人持股比例            222\n",
       "专利                 214\n",
       "商标                 220\n",
       "著作权                218\n",
       "债权融资额度2015         220\n",
       "债权融资成本2015         221\n",
       "股权融资额度2015         224\n",
       "股权融资成本2015         217\n",
       "内部融资和贸易融资额度2015    216\n",
       "内部融资和贸易融资成本2015    233\n",
       "项目融资和政策融资额度2015    212\n",
       "项目融资和政策融资成本2015    200\n",
       "债权融资额度2016         208\n",
       "债权融资成本2016         215\n",
       "股权融资额度2016         210\n",
       "股权融资成本2016         241\n",
       "内部融资和贸易融资额度2016    207\n",
       "内部融资和贸易融资成本2016    201\n",
       "项目融资和政策融资额度2016    218\n",
       "项目融资和政策融资成本2016    243\n",
       "从业人数2015           211\n",
       "资产总额2015           235\n",
       "负债总额2015           196\n",
       "营业总收入2015          244\n",
       "主营业务收入2015         199\n",
       "利润总额2015           203\n",
       "净利润2015            235\n",
       "纳税总额2015           212\n",
       "所有者权益合计2015        236\n",
       "从业人数2016           210\n",
       "资产总额2016           257\n",
       "负债总额2016           217\n",
       "营业总收入2016          202\n",
       "主营业务收入2016         199\n",
       "利润总额2016           213\n",
       "净利润2016            220\n",
       "纳税总额2016           216\n",
       "所有者权益合计2016        240\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#缺失值统计信息\n",
    "all_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>缺失比例</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>资产总额2016</th>\n",
       "      <td>1.201665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>营业总收入2015</th>\n",
       "      <td>1.140880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>项目融资和政策融资成本2016</th>\n",
       "      <td>1.136204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股权融资成本2016</th>\n",
       "      <td>1.126853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>所有者权益合计2016</th>\n",
       "      <td>1.122177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>控制人类型</th>\n",
       "      <td>1.108150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>所有者权益合计2015</th>\n",
       "      <td>1.103474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>净利润2015</th>\n",
       "      <td>1.098798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>资产总额2015</th>\n",
       "      <td>1.098798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>内部融资和贸易融资成本2015</th>\n",
       "      <td>1.089447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>注册时间</th>\n",
       "      <td>1.066068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股权融资额度2015</th>\n",
       "      <td>1.047365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>控制人持股比例</th>\n",
       "      <td>1.038014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>债权融资成本2015</th>\n",
       "      <td>1.033338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>净利润2016</th>\n",
       "      <td>1.028662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>债权融资额度2015</th>\n",
       "      <td>1.028662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>商标</th>\n",
       "      <td>1.028662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>著作权</th>\n",
       "      <td>1.019311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>项目融资和政策融资额度2016</th>\n",
       "      <td>1.019311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股权融资成本2015</th>\n",
       "      <td>1.014635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     缺失比例\n",
       "资产总额2016         1.201665\n",
       "营业总收入2015        1.140880\n",
       "项目融资和政策融资成本2016  1.136204\n",
       "股权融资成本2016       1.126853\n",
       "所有者权益合计2016      1.122177\n",
       "控制人类型            1.108150\n",
       "所有者权益合计2015      1.103474\n",
       "净利润2015          1.098798\n",
       "资产总额2015         1.098798\n",
       "内部融资和贸易融资成本2015  1.089447\n",
       "注册时间             1.066068\n",
       "股权融资额度2015       1.047365\n",
       "控制人持股比例          1.038014\n",
       "债权融资成本2015       1.033338\n",
       "净利润2016          1.028662\n",
       "债权融资额度2015       1.028662\n",
       "商标               1.028662\n",
       "著作权              1.019311\n",
       "项目融资和政策融资额度2016  1.019311\n",
       "股权融资成本2015       1.014635"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#特征缺失率\n",
    "all_data_na = (all_data.isnull().sum()/len(all_data))*100\n",
    "all_data_na = all_data_na.drop(all_data_na[all_data_na == 0].index).sort_values(ascending=False)\n",
    "missing_data = pd.DataFrame({'缺失比例' : all_data_na})\n",
    "missing_data.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>注册时间</th>\n",
       "      <th>注册资本</th>\n",
       "      <th>行业</th>\n",
       "      <th>区域</th>\n",
       "      <th>企业类型</th>\n",
       "      <th>控制人类型</th>\n",
       "      <th>控制人持股比例</th>\n",
       "      <th>专利</th>\n",
       "      <th>商标</th>\n",
       "      <th>著作权</th>\n",
       "      <th>...</th>\n",
       "      <th>所有者权益合计2015</th>\n",
       "      <th>从业人数2016</th>\n",
       "      <th>资产总额2016</th>\n",
       "      <th>负债总额2016</th>\n",
       "      <th>营业总收入2016</th>\n",
       "      <th>主营业务收入2016</th>\n",
       "      <th>利润总额2016</th>\n",
       "      <th>净利润2016</th>\n",
       "      <th>纳税总额2016</th>\n",
       "      <th>所有者权益合计2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>1190.0</td>\n",
       "      <td>服务业</td>\n",
       "      <td>湖南</td>\n",
       "      <td>农民专业合作社</td>\n",
       "      <td>自然人</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-30940.0</td>\n",
       "      <td>631.0</td>\n",
       "      <td>47600.0</td>\n",
       "      <td>69615.0</td>\n",
       "      <td>52360.0</td>\n",
       "      <td>20944.0</td>\n",
       "      <td>15708.0</td>\n",
       "      <td>-5236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-22015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>6670.0</td>\n",
       "      <td>零售业</td>\n",
       "      <td>湖南</td>\n",
       "      <td>集体所有制企业</td>\n",
       "      <td>企业法人</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>96715.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>106720.0</td>\n",
       "      <td>150075.0</td>\n",
       "      <td>74704.0</td>\n",
       "      <td>37352.0</td>\n",
       "      <td>29881.6</td>\n",
       "      <td>-7470.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-43355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>9100.0</td>\n",
       "      <td>社区服务</td>\n",
       "      <td>湖北</td>\n",
       "      <td>农民专业合作社</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.51</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-109200.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>291200.0</td>\n",
       "      <td>282100.0</td>\n",
       "      <td>145600.0</td>\n",
       "      <td>87360.0</td>\n",
       "      <td>29120.0</td>\n",
       "      <td>43680.0</td>\n",
       "      <td>87360.0</td>\n",
       "      <td>9100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2002.0</td>\n",
       "      <td>5870.0</td>\n",
       "      <td>商业服务业</td>\n",
       "      <td>广东</td>\n",
       "      <td>股份有限公司</td>\n",
       "      <td>企业法人</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-29350.0</td>\n",
       "      <td>965.0</td>\n",
       "      <td>410900.0</td>\n",
       "      <td>607545.0</td>\n",
       "      <td>1150520.0</td>\n",
       "      <td>920416.0</td>\n",
       "      <td>115052.0</td>\n",
       "      <td>230104.0</td>\n",
       "      <td>230104.0</td>\n",
       "      <td>-196645.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005.0</td>\n",
       "      <td>9850.0</td>\n",
       "      <td>工业</td>\n",
       "      <td>江西</td>\n",
       "      <td>集体所有制企业</td>\n",
       "      <td>企业法人</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-78800.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>325050.0</td>\n",
       "      <td>472800.0</td>\n",
       "      <td>1137675.0</td>\n",
       "      <td>682605.0</td>\n",
       "      <td>341302.5</td>\n",
       "      <td>341302.5</td>\n",
       "      <td>455070.0</td>\n",
       "      <td>-147750.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     注册时间    注册资本     行业  区域     企业类型 控制人类型  控制人持股比例   专利   商标  著作权  ...  \\\n",
       "0  2008.0  1190.0    服务业  湖南  农民专业合作社   自然人     0.91  1.0  NaN  0.0  ...   \n",
       "1  2008.0  6670.0    零售业  湖南  集体所有制企业  企业法人     0.91  0.0  0.0  0.0  ...   \n",
       "2  2008.0  9100.0   社区服务  湖北  农民专业合作社   NaN     0.51  1.0  0.0  1.0  ...   \n",
       "3  2002.0  5870.0  商业服务业  广东   股份有限公司  企业法人     0.63  1.0  0.0  1.0  ...   \n",
       "4  2005.0  9850.0     工业  江西  集体所有制企业  企业法人     0.58  0.0  0.0  0.0  ...   \n",
       "\n",
       "   所有者权益合计2015  从业人数2016  资产总额2016  负债总额2016  营业总收入2016  主营业务收入2016  利润总额2016  \\\n",
       "0     -30940.0     631.0   47600.0   69615.0    52360.0     20944.0   15708.0   \n",
       "1      96715.0     353.0  106720.0  150075.0    74704.0     37352.0   29881.6   \n",
       "2    -109200.0      84.0  291200.0  282100.0   145600.0     87360.0   29120.0   \n",
       "3     -29350.0     965.0  410900.0  607545.0  1150520.0    920416.0  115052.0   \n",
       "4     -78800.0     213.0  325050.0  472800.0  1137675.0    682605.0  341302.5   \n",
       "\n",
       "    净利润2016  纳税总额2016  所有者权益合计2016  \n",
       "0   -5236.0       0.0     -22015.0  \n",
       "1   -7470.4       0.0     -43355.0  \n",
       "2   43680.0   87360.0       9100.0  \n",
       "3  230104.0  230104.0    -196645.0  \n",
       "4  341302.5  455070.0    -147750.0  \n",
       "\n",
       "[5 rows x 44 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义填充方法\n",
    "def fill_with_mode(df):\n",
    "    cols=df.columns\n",
    "    for col in cols:\n",
    "        df[col]=df[col].fillna(df[col].mode()[0])\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#用众数填补缺失值\n",
    "train322=fill_with_mode(train31)\n",
    "test322=fill_with_mode(test31)\n",
    "all_data322=fill_with_mode(all_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.2.3 数据相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1080x864 with 1 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x1d0cf49e788>)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d0cf49e788>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 全部数值特征\n",
    "corrmat = train322.corr()\n",
    "plt.subplots(figsize=(15,12))\n",
    "sns.heatmap(corrmat, vmax=0.9, square=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 720x576 with 1 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x1d0d145f448>)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d0d145f448>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 相关性最大的10个特征\n",
    "corrmat = train322.corr()\n",
    "plt.subplots(figsize=(10,8))\n",
    "k = 10\n",
    "cols = corrmat.nlargest(k, '净利润2017')['净利润2017'].index\n",
    "cm = np.corrcoef(train322[cols].values.T)\n",
    "sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.2.4 标签编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "#待编码的特征\n",
    "cols_encode=['注册时间']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将数值类型（数值作为类型的列）转化为字符串类型\n",
    "feature=['注册时间']\n",
    "for i in feature:\n",
    "    train[i]=train[i].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "#对不连续的数字或文本进行编号，转换成连续的数值型变量\n",
    "for c in cols_encode:\n",
    "    le=LabelEncoder()\n",
    "    all_data322[c]=le.fit_transform(list(all_data322[c].values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>注册时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   注册时间\n",
       "0     8\n",
       "1     8\n",
       "2     8\n",
       "3     2\n",
       "4     5"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看编码后的情况\n",
    "all_data322[cols_encode].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.2.5 独热编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
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       "      <th>行业</th>\n",
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       "      <th>企业类型</th>\n",
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       "      <th>负债总额2016</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-109200.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>291200.0</td>\n",
       "      <td>282100.0</td>\n",
       "      <td>145600.0</td>\n",
       "      <td>87360.0</td>\n",
       "      <td>29120.0</td>\n",
       "      <td>43680.0</td>\n",
       "      <td>87360.0</td>\n",
       "      <td>9100.0</td>\n",
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       "      <th>4</th>\n",
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       "   注册时间    注册资本  行业  区域  企业类型  控制人类型  控制人持股比例   专利   商标  著作权  ...  \\\n",
       "0     8  1190.0   3   5     0      1     0.91  1.0  0.0  0.0  ...   \n",
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       "2     8  9100.0   4   4     0      0     0.51  1.0  0.0  1.0  ...   \n",
       "3     2  5870.0   1   1     3      0     0.63  1.0  0.0  1.0  ...   \n",
       "4     5  9850.0   2   3     4      0     0.58  0.0  0.0  0.0  ...   \n",
       "\n",
       "   所有者权益合计2015  从业人数2016  资产总额2016  负债总额2016  营业总收入2016  主营业务收入2016  利润总额2016  \\\n",
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       "2    -109200.0      84.0  291200.0  282100.0   145600.0     87360.0   29120.0   \n",
       "3     -29350.0     965.0  410900.0  607545.0  1150520.0    920416.0  115052.0   \n",
       "4     -78800.0     213.0  325050.0  472800.0  1137675.0    682605.0  341302.5   \n",
       "\n",
       "    净利润2016  纳税总额2016  所有者权益合计2016  \n",
       "0   -5236.0       0.0     -22015.0  \n",
       "1   -7470.4       0.0     -43355.0  \n",
       "2   43680.0   87360.0       9100.0  \n",
       "3  230104.0  230104.0    -196645.0  \n",
       "4  341302.5  455070.0    -147750.0  \n",
       "\n",
       "[5 rows x 44 columns]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 独热编码用来解决类别型数据的离散值问题\n",
    "all_data325 = pd.get_dummies(all_data322)\n",
    "all_data325.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.2.6 重新划分数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将处理好的数据重新划分成训练集和测试集，为模型训练与测试做准备\n",
    "train326 = all_data325[:ntrain]\n",
    "test326 = all_data325[ntrain:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 3.2.7 特征重要性检测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LERRY\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 126347730693849.92, tolerance: 28415992188.507782\n",
      "  positive)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=0.001, copy_X=True, fit_intercept=True, max_iter=1000,\n",
       "      normalize=False, positive=False, precompute=False, random_state=None,\n",
       "      selection='cyclic', tol=0.0001, warm_start=False)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso=Lasso(alpha=0.001)\n",
    "lasso.fit(train326,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>0.041624</td>\n",
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       "      <td>0.036664</td>\n",
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       "      <td>0.015696</td>\n",
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       "      <th>营业总收入2015</th>\n",
       "      <td>0.013726</td>\n",
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       "    <tr>\n",
       "      <th>纳税总额2016</th>\n",
       "      <td>0.012255</td>\n",
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       "    <tr>\n",
       "      <th>主营业务收入2016</th>\n",
       "      <td>0.010307</td>\n",
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       "    <tr>\n",
       "      <th>所有者权益合计2015</th>\n",
       "      <td>0.008274</td>\n",
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       "      <th>纳税总额2015</th>\n",
       "      <td>0.002295</td>\n",
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       "    <tr>\n",
       "      <th>净利润2015</th>\n",
       "      <td>0.001475</td>\n",
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       "      <th>主营业务收入2015</th>\n",
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       "      <th>营业总收入2016</th>\n",
       "      <td>-0.012219</td>\n",
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       "      <th>利润总额2015</th>\n",
       "      <td>-0.031221</td>\n",
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       "      <th>净利润2016</th>\n",
       "      <td>-0.042845</td>\n",
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       "      <th>股权融资额度2016</th>\n",
       "      <td>-0.089566</td>\n",
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       "    <tr>\n",
       "      <th>内部融资和贸易融资额度2015</th>\n",
       "      <td>-0.173665</td>\n",
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       "    <tr>\n",
       "      <th>股权融资额度2015</th>\n",
       "      <td>-0.411562</td>\n",
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       "      <th>债权融资额度2016</th>\n",
       "      <td>-0.477694</td>\n",
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       "    <tr>\n",
       "      <th>内部融资和贸易融资成本2016</th>\n",
       "      <td>-4.349163</td>\n",
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       "    <tr>\n",
       "      <th>从业人数2016</th>\n",
       "      <td>-4.876867</td>\n",
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       "    <tr>\n",
       "      <th>项目融资和政策融资成本2015</th>\n",
       "      <td>-12.436486</td>\n",
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       "    <tr>\n",
       "      <th>项目融资和政策融资成本2016</th>\n",
       "      <td>-14.322888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>区域</th>\n",
       "      <td>-15.633030</td>\n",
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       "    <tr>\n",
       "      <th>债权融资成本2015</th>\n",
       "      <td>-16.094931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>控制人持股比例</th>\n",
       "      <td>-209.112423</td>\n",
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       "    <tr>\n",
       "      <th>专利</th>\n",
       "      <td>-273.485066</td>\n",
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       "    <tr>\n",
       "      <th>行业</th>\n",
       "      <td>-308.161493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>商标</th>\n",
       "      <td>-810.487969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>控制人类型</th>\n",
       "      <td>-1204.352113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>注册时间</th>\n",
       "      <td>-3478.315782</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       特征重要性\n",
       "著作权              1668.159810\n",
       "企业类型              143.605531\n",
       "注册资本                7.996649\n",
       "股权融资成本2015          6.415303\n",
       "债权融资成本2016          3.943163\n",
       "内部融资和贸易融资成本2015     2.838004\n",
       "从业人数2015            2.762574\n",
       "债权融资额度2015          1.258808\n",
       "项目融资和政策融资额度2016     1.013722\n",
       "股权融资成本2016          0.578048\n",
       "项目融资和政策融资额度2015     0.344947\n",
       "内部融资和贸易融资额度2016     0.277150\n",
       "资产总额2016            0.044915\n",
       "资产总额2015            0.041624\n",
       "负债总额2015            0.036664\n",
       "利润总额2016            0.027135\n",
       "负债总额2016            0.019151\n",
       "所有者权益合计2016         0.015696\n",
       "营业总收入2015           0.013726\n",
       "纳税总额2016            0.012255\n",
       "主营业务收入2016          0.010307\n",
       "所有者权益合计2015         0.008274\n",
       "纳税总额2015            0.002295\n",
       "净利润2015             0.001475\n",
       "主营业务收入2015         -0.005207\n",
       "营业总收入2016          -0.012219\n",
       "利润总额2015           -0.031221\n",
       "净利润2016            -0.042845\n",
       "股权融资额度2016         -0.089566\n",
       "内部融资和贸易融资额度2015    -0.173665\n",
       "股权融资额度2015         -0.411562\n",
       "债权融资额度2016         -0.477694\n",
       "内部融资和贸易融资成本2016    -4.349163\n",
       "从业人数2016           -4.876867\n",
       "项目融资和政策融资成本2015   -12.436486\n",
       "项目融资和政策融资成本2016   -14.322888\n",
       "区域                -15.633030\n",
       "债权融资成本2015        -16.094931\n",
       "控制人持股比例          -209.112423\n",
       "专利               -273.485066\n",
       "行业               -308.161493\n",
       "商标               -810.487969\n",
       "控制人类型           -1204.352113\n",
       "注册时间            -3478.315782"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FI_lasso = pd.DataFrame({\"特征重要性\":lasso.coef_}, index=train326.columns)\n",
    "FI_lasso.sort_values(\"特征重要性\",ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['著作权',\n",
       " '企业类型',\n",
       " '注册资本',\n",
       " '从业人数2015',\n",
       " '资产总额2015',\n",
       " '负债总额2015',\n",
       " '营业总收入2015',\n",
       " '主营业务收入2015',\n",
       " '利润总额2015',\n",
       " '净利润2015',\n",
       " '纳税总额2015',\n",
       " '所有者权益合计2015',\n",
       " '从业人数2016',\n",
       " '资产总额2016',\n",
       " '负债总额2016',\n",
       " '营业总收入2016',\n",
       " '主营业务收入2016',\n",
       " '利润总额2016',\n",
       " '净利润2016',\n",
       " '纳税总额2016',\n",
       " '所有者权益合计2016',\n",
       " '债权融资额度2015',\n",
       " '债权融资成本2015',\n",
       " '股权融资额度2015',\n",
       " '股权融资成本2015',\n",
       " '内部融资和贸易融资额度2015',\n",
       " '内部融资和贸易融资成本2015',\n",
       " '项目融资和政策融资额度2015',\n",
       " '项目融资和政策融资成本2015',\n",
       " '债权融资额度2016',\n",
       " '债权融资成本2016',\n",
       " '股权融资额度2016',\n",
       " '股权融资成本2016',\n",
       " '内部融资和贸易融资额度2016',\n",
       " '内部融资和贸易融资成本2016',\n",
       " '项目融资和政策融资额度2016',\n",
       " '项目融资和政策融资成本2016']"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#只保存较重要的特征\n",
    "cols327_1=year_train_res.columns.drop(['ID','净利润2017', '从业人数2017','资产总额2017','负债总额2017','营业总收入2017','主营业务收入2017','利润总额2017','纳税总额2017','所有者权益合计2017']).values.tolist()\n",
    "cols327_2=money_train_res.columns.drop(['ID','债权融资额度2017','债权融资成本2017','股权融资额度2017','股权融资成本2017', '内部融资和贸易融资额度2017','内部融资和贸易融资成本2017','项目融资和政策融资额度2017','项目融资和政策融资成本2017']).values.tolist()\n",
    "cols327_1.extend(cols327_2)\n",
    "cols327=['著作权','企业类型','注册资本']\n",
    "cols327.extend(cols327_1)\n",
    "cols327"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data327=all_data325.loc[:,cols327].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将处理好的数据重新划分成训练集和测试集，为模型训练与测试做准备\n",
    "train327 = all_data327[:ntrain]\n",
    "test327 = all_data327[ntrain:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17319, 37)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(4068, 37)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train327.shape\n",
    "test327.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 基础模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 定义交叉验证策略"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cross_val_score默认使用K折交叉验证策略。此处先使用KFold的shuffle参数混洗数据\n",
    "# neg_mean_squared_error：负均方误差，是损失函数，优化目标是使其最小化\n",
    "n_splits = 5\n",
    "def nmse_cv(model,train,y_train):\n",
    "    kf = KFold(n_splits, shuffle=True, random_state=42).get_n_splits(train)\n",
    "    nmse = np.sqrt(-cross_val_score(model, train, y_train, scoring='neg_mean_squared_error', cv=kf))\n",
    "    return(nmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 建立基础模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_xgb = xgb.XGBRegressor(colsample_bytree=0.4603, gamma=0.0468, \n",
    "                             learning_rate=0.05, max_depth=3, \n",
    "                             min_child_weight=1.7817, n_estimators=2200,\n",
    "                             reg_alpha=0.4640, reg_lambda=0.8571,\n",
    "                             subsample=0.5213, silent=1,\n",
    "                             random_state =7, nthread = -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 基础模型分数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "#y_train归一化\n",
    "mm=MinMaxScaler()\n",
    "y_train43=mm.fit_transform(y_train.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train43=train327.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model_xgb score:0.0674 (0.0004) \n",
      "\n"
     ]
    }
   ],
   "source": [
    "score = nmse_cv(model_xgb,x_train43,y_train43)\n",
    "# 验证结果返回1个分数，求均值和标准差\n",
    "print('model_xgb score:{:.4f} ({:.4f}) \\n'.format(score.mean(), score.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 模型训练与预测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 定义评估函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 均方差：MSE的值越小，预测模型具有更好的精确度\n",
    "def mse(y, y_pred):\n",
    "    return np.sqrt(mean_squared_error(y, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 模型训练、评估、预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "#训练集测试集8:2划分\n",
    "X_train,X_test,y_train,y_test=train_test_split(x_train43, y_train43, test_size=0.2, random_state=2020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train (13855, 37)\n",
      "y_train (13855, 1)\n",
      "X_test (3464, 37)\n",
      "y_test (3464, 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.0000e+00, 2.0000e+00, 2.2000e+02, ..., 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00],\n",
       "       [0.0000e+00, 1.0000e+00, 8.3000e+02, ..., 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00],\n",
       "       [0.0000e+00, 1.0000e+00, 2.1800e+03, ..., 7.8480e+02, 0.0000e+00,\n",
       "        0.0000e+00],\n",
       "       ...,\n",
       "       [0.0000e+00, 3.0000e+00, 8.9700e+03, ..., 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00],\n",
       "       [0.0000e+00, 1.0000e+00, 9.0800e+03, ..., 0.0000e+00, 3.6320e+02,\n",
       "        2.1792e+01],\n",
       "       [0.0000e+00, 3.0000e+00, 5.1300e+03, ..., 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00]])"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('X_train',X_train.shape)\n",
    "print('y_train',y_train.shape)\n",
    "print('X_test',X_test.shape)\n",
    "print('y_test',y_test.shape)\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster=None, colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=0.4603, gamma=0.0468,\n",
       "             gpu_id=-1, importance_type='gain', interaction_constraints=None,\n",
       "             learning_rate=0.05, max_delta_step=0, max_depth=3,\n",
       "             min_child_weight=1.7817, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=2200, n_jobs=-1, nthread=-1, num_parallel_tree=1,\n",
       "             objective='reg:squarederror', random_state=7, reg_alpha=0.464,\n",
       "             reg_lambda=0.8571, scale_pos_weight=1, silent=1, subsample=0.5213,\n",
       "             tree_method=None, validate_parameters=False, verbosity=None)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0710739186571407\n"
     ]
    }
   ],
   "source": [
    "model_xgb.fit(X_train, y_train)\n",
    "xgb_train_pred = model_xgb.predict(X_test)\n",
    "print(mse(y_test, xgb_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "#模型保存\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data//enterprise_profit_forecast/model/xgb.model']"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(model_xgb,'data//enterprise_profit_forecast/model/xgb.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#加载模型\n",
    "# model_xgb=joblib.load('data//enterprise_profit_forecast/model/xgb.model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 生成结果文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "pre52=test327.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_pred = np.expm1(model_xgb.predict(pre52))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.2827106 , 0.26691648, 0.2659227 , ..., 0.2593621 , 0.31098577,\n",
       "       0.2810961 ], dtype=float32)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = pd.DataFrame()\n",
    "res['Id'] = test_Id\n",
    "res['净利润'] = xgb_pred\n",
    "res.to_csv('data/enterprise_profit_forecast/output/res.txt', index=False)"
   ]
  }
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